The World Economic Forum reports that 41% of employers plan workforce reductions by 2030 due to AI. Amazon’s CEO just told employees that AI will mean “fewer people doing some jobs,” while Microsoft is cutting 6,000 workers after pushing AI adoption. They’re about to make the biggest strategic mistake of the decade.
The real choice leaders face today isn’t which AI model to buy—it’s how to use it: as a layoff machine, or as a competitive advantage to eat their rivals’ lunch.
We’ve seen this before. At my last job at Cloudability, I witnessed a fascinating SaaS dynamic: businesses that innovate take cost savings and reinvest them back into efficiency, reach, and market share.
Our cloud cost management product rarely saved businesses money on their bills. But it did give them insights to optimize spend and allowed them to reinvest savings into bigger bets and innovation. The companies that did so went on to become innovators and leaders; the ones that didn’t became footnotes in someone else’s growth story.
Replacement vs. Redeployment
The same pattern is emerging with AI agents, except this time the savings aren’t budget dollars—they’re human time, capability, and potential. According to new research from Salesforce, AI agent adoption is expected to increase 327% over the next two years, leading to productivity gains of 30%. The shift isn’t toward replacement—it’s toward redeployment.
When AI handles repetitive tasks like rerouting customer support tickets, summarizing meeting notes, or filling out compliance reports, it doesn’t replace jobs, it frees up capacity. CHROs expect to redeploy 23% of their workforce to new roles and 89% believe AI will empower them to reassign employees to new, more relevant positions.
While those findings offer promise and reassurance for the workforce, not every leader will see it that way. Based on my experience using AI as a force multiplier for my team, I believe the companies that thrive will be the ones that figure out how to integrate humans and digital teammates in ways that maximize both efficiency and the irreplaceable qualities machines can’t replicate—like creativity, empathy, and judgment.
The AI Divide
This sets up a fundamental choice: cut or invest. But beneath that lies a deeper philosophical question: Do you see people as a cost to manage or a force to grow your business?
In the case of a contact center, a leader with a cost-cutting mindset might say “Let’s replace our support team with chatbots to save money.” That decision might frustrate customers and drive up escalations, leading to short-term savings at the expense of long-term growth.
The reinvestment approach sounds more like: “Let’s use AI for case classification and routing so customers get the right expert with all context preloaded.” The result is faster resolution times, happier customers, support agents focused on higher-value work—and more sustainable business growth.
The productivity gains are real—what sets winning leaders apart is asking, “How do we reinvest this efficiency and grow?” “Where can we shift human focus from tasks to transformation?” and “How do we redesign work to create more value, not just more output?”
Review, Reallocate, and Reimagine
For those leaders ready to grow and build something better, I recommend the three Rs.
- Review: Audit employee time usage, identify underused tools, and diagnose process bottlenecks. Determine which tasks are repetitive or low-value and which ones can be automated with AI agents.
- Reallocate: Reclaim hours unlocked by AI and reinvest them in work that accelerates revenue, deepens customer value, or fuels innovation.
- Reimagine: Shift time toward high-impact interactions, retrain agents for sales roles, or embed support teams into product development to help eliminate root-cause customer issues.
AI will amplify an existing leadership philosophy. If a leader is already shortsighted about people and customers, AI will make that worse. If leaders invest in their teams and customer experience, AI will become a force multiplier for both.
This difference in approach explains why identical AI implementations can produce vastly different business outcomes. The technology is the same, but the leadership philosophy determines whether AI becomes a race to the bottom or a rocket to the top.
